Trends are the overall direction your data is moving in over time - going up, down or staying flat.
- Differencing in Time Series: Subtract the previous value from the current value: Removes linear trends. Can apply higher-order differencing if needed.
- Detrending with regression: Fit a line or curve to the data and use residuals.
- Transformation: Log transformation or square root to stabilize growth rates.
In Exponential Smoothing can add a dampening term to. Which applies a factor (0 < < 1) that slows down the trend over time:
- Damping is useful to avoid overly optimistic or pessimistic forecasts. Without damping, trends extend linearly into the future, which can be unrealistic (e.g., unchecked growth). A damped trend gradually flattens, reflecting real-world limits (like market saturation). This is especially relevant for the customer growth series.